Alias Analysis (aka Pointer Analysis) is a class of techniques which attempt to
determine whether or not two pointers ever can point to the same object in
memory. There are many different algorithms for alias analysis and many
different ways of classifying them: flow-sensitive vs. flow-insensitive,
context-sensitive vs. context-insensitive, field-sensitive
vs. field-insensitive, unification-based vs. subset-based, etc. Traditionally,
alias analyses respond to a query with a Must, May, or No alias response,
indicating that two pointers always point to the same object, might point to the
same object, or are known to never point to the same object.

The LLVM AliasAnalysis class is the
primary interface used by clients and implementations of alias analyses in the
LLVM system. This class is the common interface between clients of alias
analysis information and the implementations providing it, and is designed to
support a wide range of implementations and clients (but currently all clients
are assumed to be flow-insensitive). In addition to simple alias analysis
information, this class exposes Mod/Ref information from those implementations
which can provide it, allowing for powerful analyses and transformations to work
well together.

This document contains information necessary to successfully implement this
interface, use it, and to test both sides. It also explains some of the finer
points about what exactly results mean. If you feel that something is unclear
or should be added, please let me know.

The AliasAnalysis
class defines the interface that the various alias analysis implementations
should support. This class exports two important enums: AliasResult and
ModRefResult which represent the result of an alias query or a mod/ref
query, respectively.

The AliasAnalysis interface exposes information about memory, represented in
several different ways. In particular, memory objects are represented as a
starting address and size, and function calls are represented as the actual
call or invoke instructions that performs the call. The
AliasAnalysis interface also exposes some helper methods which allow you to
get mod/ref information for arbitrary instructions.

All AliasAnalysis interfaces require that in queries involving multiple
values, values which are not constants are all
defined within the same function.

Most importantly, the AliasAnalysis class provides several methods which are
used to query whether or not two memory objects alias, whether function calls
can modify or read a memory object, etc. For all of these queries, memory
objects are represented as a pair of their starting address (a symbolic LLVM
Value*) and a static size.

Representing memory objects as a starting address and a size is critically
important for correct Alias Analyses. For example, consider this (silly, but
possible) C code:

In this case, the basicaa pass will disambiguate the stores to C[0] and
C[1] because they are accesses to two distinct locations one byte apart, and
the accesses are each one byte. In this case, the Loop Invariant Code Motion
(LICM) pass can use store motion to remove the stores from the loop. In
constrast, the following code:

In this case, the two stores to C do alias each other, because the access to the
&C[0] element is a two byte access. If size information wasn’t available in
the query, even the first case would have to conservatively assume that the
accesses alias.

The alias method is the primary interface used to determine whether or not
two memory objects alias each other. It takes two memory objects as input and
returns MustAlias, PartialAlias, MayAlias, or NoAlias as appropriate.

Like all AliasAnalysis interfaces, the alias method requires that either
the two pointer values be defined within the same function, or at least one of
the values is a constant.

The NoAlias response may be used when there is never an immediate dependence
between any memory reference based on one pointer and any memory reference
based the other. The most obvious example is when the two pointers point to
non-overlapping memory ranges. Another is when the two pointers are only ever
used for reading memory. Another is when the memory is freed and reallocated
between accesses through one pointer and accesses through the other — in this
case, there is a dependence, but it’s mediated by the free and reallocation.

As an exception to this is with the noalias keyword;
the “irrelevant” dependencies are ignored.

The MayAlias response is used whenever the two pointers might refer to the
same object.

The PartialAlias response is used when the two memory objects are known to
be overlapping in some way, but do not start at the same address.

The MustAlias response may only be returned if the two memory objects are
guaranteed to always start at exactly the same location. A MustAlias
response implies that the pointers compare equal.

The getModRefInfo methods return information about whether the execution of
an instruction can read or modify a memory location. Mod/Ref information is
always conservative: if an instruction might read or write a location,
ModRef is returned.

The AliasAnalysis class also provides a getModRefInfo method for testing
dependencies between function calls. This method takes two call sites (CS1
& CS2), returns NoModRef if neither call writes to memory read or
written by the other, Ref if CS1 reads memory written by CS2,
Mod if CS1 writes to memory read or written by CS2, or ModRef if
CS1 might read or write memory written to by CS2. Note that this
relation is not commutative.

The pointsToConstantMemory method returns true if and only if the analysis
can prove that the pointer only points to unchanging memory locations
(functions, constant global variables, and the null pointer). This information
can be used to refine mod/ref information: it is impossible for an unchanging
memory location to be modified.

These methods are used to provide very simple mod/ref information for function
calls. The doesNotAccessMemory method returns true for a function if the
analysis can prove that the function never reads or writes to memory, or if the
function only reads from constant memory. Functions with this property are
side-effect free and only depend on their input arguments, allowing them to be
eliminated if they form common subexpressions or be hoisted out of loops. Many
common functions behave this way (e.g., sin and cos) but many others do
not (e.g., acos, which modifies the errno variable).

The onlyReadsMemory method returns true for a function if analysis can prove
that (at most) the function only reads from non-volatile memory. Functions with
this property are side-effect free, only depending on their input arguments and
the state of memory when they are called. This property allows calls to these
functions to be eliminated and moved around, as long as there is no store
instruction that changes the contents of memory. Note that all functions that
satisfy the doesNotAccessMemory method also satisfies onlyReadsMemory.

Writing a new alias analysis implementation for LLVM is quite straight-forward.
There are already several implementations that you can use for examples, and the
following information should help fill in any details. For a examples, take a
look at the various alias analysis implementations included with LLVM.

The first step to determining what type of LLVM pass
you need to use for your Alias Analysis. As is the case with most other
analyses and transformations, the answer should be fairly obvious from what type
of problem you are trying to solve:

If you require interprocedural analysis, it should be a Pass.

If you are a function-local analysis, subclass FunctionPass.

If you don’t need to look at the program at all, subclass ImmutablePass.

In addition to the pass that you subclass, you should also inherit from the
AliasAnalysis interface, of course, and use the RegisterAnalysisGroup
template to register as an implementation of AliasAnalysis.

Your subclass of AliasAnalysis is required to invoke two methods on the
AliasAnalysis base class: getAnalysisUsage and
InitializeAliasAnalysis. In particular, your implementation of
getAnalysisUsage should explicitly call into the
AliasAnalysis::getAnalysisUsage method in addition to doing any declaring
any pass dependencies your pass has. Thus you should have something like this:

Additionally, your must invoke the InitializeAliasAnalysis method from your
analysis run method (run for a Pass, runOnFunction for a
FunctionPass, or InitializePass for an ImmutablePass). For example
(as part of a Pass):

All of the AliasAnalysis virtual methods
default to providing chaining to another alias
analysis implementation, which ends up returning conservatively correct
information (returning “May” Alias and “Mod/Ref” for alias and mod/ref queries
respectively). Depending on the capabilities of the analysis you are
implementing, you just override the interfaces you can improve.

With only one special exception (the -no-aa pass)
every alias analysis pass chains to another alias analysis implementation (for
example, the user can specify “-basicaa-ds-aa-licm” to get the maximum
benefit from both alias analyses). The alias analysis class automatically
takes care of most of this for methods that you don’t override. For methods
that you do override, in code paths that return a conservative MayAlias or
Mod/Ref result, simply return whatever the superclass computes. For example:

AliasAnalysis::AliasResultalias(constValue*V1,unsignedV1Size,constValue*V2,unsignedV2Size){if(...)returnNoAlias;...// Couldn't determine a must or no-alias result.returnAliasAnalysis::alias(V1,V1Size,V2,V2Size);}

In addition to analysis queries, you must make sure to unconditionally pass LLVM
update notification methods to the superclass as well if you override them,
which allows all alias analyses in a change to be updated.

Alias analysis information is initially computed for a static snapshot of the
program, but clients will use this information to make transformations to the
code. All but the most trivial forms of alias analysis will need to have their
analysis results updated to reflect the changes made by these transformations.

The AliasAnalysis interface exposes four methods which are used to
communicate program changes from the clients to the analysis implementations.
Various alias analysis implementations should use these methods to ensure that
their internal data structures are kept up-to-date as the program changes (for
example, when an instruction is deleted), and clients of alias analysis must be
sure to call these interfaces appropriately.

The deleteValue method is called by transformations when they remove an
instruction or any other value from the program (including values that do not
use pointers). Typically alias analyses keep data structures that have entries
for each value in the program. When this method is called, they should remove
any entries for the specified value, if they exist.

The copyValue method is used when a new value is introduced into the
program. There is no way to introduce a value into the program that did not
exist before (this doesn’t make sense for a safe compiler transformation), so
this is the only way to introduce a new value. This method indicates that the
new value has exactly the same properties as the value being copied.

This method is a simple helper method that is provided to make clients easier to
use. It is implemented by copying the old analysis information to the new
value, then deleting the old value. This method cannot be overridden by alias
analysis implementations.

The addEscapingUse method is used when the uses of a pointer value have
changed in ways that may invalidate precomputed analysis information.
Implementations may either use this callback to provide conservative responses
for points whose uses have change since analysis time, or may recompute some or
all of their internal state to continue providing accurate responses.

In general, any new use of a pointer value is considered an escaping use, and
must be reported through this callback, except for the uses below:

From the LLVM perspective, the only thing you need to do to provide an efficient
alias analysis is to make sure that alias analysis queries are serviced
quickly. The actual calculation of the alias analysis results (the “run”
method) is only performed once, but many (perhaps duplicate) queries may be
performed. Because of this, try to move as much computation to the run method
as possible (within reason).

The AliasAnalysis infrastructure has several limitations which make writing a
new AliasAnalysis implementation difficult.

There is no way to override the default alias analysis. It would be very useful
to be able to do something like “opt-my-aa-O2” and have it use -my-aa
for all passes which need AliasAnalysis, but there is currently no support for
that, short of changing the source code and recompiling. Similarly, there is
also no way of setting a chain of analyses as the default.

There is no way for transform passes to declare that they preserve
AliasAnalysis implementations. The AliasAnalysis interface includes
deleteValue and copyValue methods which are intended to allow a pass to
keep an AliasAnalysis consistent, however there’s no way for a pass to declare
in its getAnalysisUsage that it does so. Some passes attempt to use
AU.addPreserved<AliasAnalysis>, however this doesn’t actually have any
effect.

AliasAnalysisCounter (-count-aa) and AliasDebugger (-debug-aa)
are implemented as ModulePass classes, so if your alias analysis uses
FunctionPass, it won’t be able to use these utilities. If you try to use
them, the pass manager will silently route alias analysis queries directly to
BasicAliasAnalysis instead.

Similarly, the opt-p option introduces ModulePass passes between each
pass, which prevents the use of FunctionPass alias analysis passes.

The AliasAnalysis API does have functions for notifying implementations when
values are deleted or copied, however these aren’t sufficient. There are many
other ways that LLVM IR can be modified which could be relevant to
AliasAnalysis implementations which can not be expressed.

The AliasAnalysisDebugger utility seems to suggest that AliasAnalysis
implementations can expect that they will be informed of any relevant Value
before it appears in an alias query. However, popular clients such as GVN
don’t support this, and are known to trigger errors when run with the
AliasAnalysisDebugger.

Due to several of the above limitations, the most obvious use for the
AliasAnalysisCounter utility, collecting stats on all alias queries in a
compilation, doesn’t work, even if the AliasAnalysis implementations don’t
use FunctionPass. There’s no way to set a default, much less a default
sequence, and there’s no way to preserve it.

The AliasSetTracker class (which is used by LICM) makes a
non-deterministic number of alias queries. This can cause stats collected by
AliasAnalysisCounter to have fluctuations among identical runs, for
example. Another consequence is that debugging techniques involving pausing
execution after a predetermined number of queries can be unreliable.

Many alias queries can be reformulated in terms of other alias queries. When
multiple AliasAnalysis queries are chained together, it would make sense to
start those queries from the beginning of the chain, with care taken to avoid
infinite looping, however currently an implementation which wants to do this can
only start such queries from itself.

The memdep pass uses alias analysis to provide high-level dependence
information about memory-using instructions. This will tell you which store
feeds into a load, for example. It uses caching and other techniques to be
efficient, and is used by Dead Store Elimination, GVN, and memcpy optimizations.

Many transformations need information about alias sets that are active in
some scope, rather than information about pairwise aliasing. The
AliasSetTracker
class is used to efficiently build these Alias Sets from the pairwise alias
analysis information provided by the AliasAnalysis interface.

First you initialize the AliasSetTracker by using the “add” methods to add
information about various potentially aliasing instructions in the scope you are
interested in. Once all of the alias sets are completed, your pass should
simply iterate through the constructed alias sets, using the AliasSetTrackerbegin()/end() methods.

The AliasSets formed by the AliasSetTracker are guaranteed to be
disjoint, calculate mod/ref information and volatility for the set, and keep
track of whether or not all of the pointers in the set are Must aliases. The
AliasSetTracker also makes sure that sets are properly folded due to call
instructions, and can provide a list of pointers in each set.

As an example user of this, the Loop Invariant Code Motion pass uses AliasSetTrackers to calculate alias
sets for each loop nest. If an AliasSet in a loop is not modified, then all
load instructions from that set may be hoisted out of the loop. If any alias
sets are stored to and are must alias sets, then the stores may be sunk
to outside of the loop, promoting the memory location to a register for the
duration of the loop nest. Both of these transformations only apply if the
pointer argument is loop-invariant.

The AliasSetTracker class is implemented to be as efficient as possible. It
uses the union-find algorithm to efficiently merge AliasSets when a pointer is
inserted into the AliasSetTracker that aliases multiple sets. The primary data
structure is a hash table mapping pointers to the AliasSet they are in.

The AliasSetTracker class must maintain a list of all of the LLVM Value*s
that are in each AliasSet. Since the hash table already has entries for each
LLVM Value* of interest, the AliasesSets thread the linked list through
these hash-table nodes to avoid having to allocate memory unnecessarily, and to
make merging alias sets extremely efficient (the linked list merge is constant
time).

You shouldn’t need to understand these details if you are just a client of the
AliasSetTracker, but if you look at the code, hopefully this brief description
will help make sense of why things are designed the way they are.

If neither of these utility class are what your pass needs, you should use the
interfaces exposed by the AliasAnalysis class directly. Try to use the
higher-level methods when possible (e.g., use mod/ref information instead of the
alias method directly if possible) to get the best precision and efficiency.

If you’re going to be working with the LLVM alias analysis infrastructure, you
should know what clients and implementations of alias analysis are available.
In particular, if you are implementing an alias analysis, you should be aware of
the the clients that are useful for monitoring and evaluating different
implementations.

The -no-aa pass is just like what it sounds: an alias analysis that never
returns any useful information. This pass can be useful if you think that alias
analysis is doing something wrong and are trying to narrow down a problem.

This pass implements a simple context-sensitive mod/ref and alias analysis for
internal global variables that don’t “have their address taken”. If a global
does not have its address taken, the pass knows that no pointers alias the
global. This pass also keeps track of functions that it knows never access
memory or never read memory. This allows certain optimizations (e.g. GVN) to
eliminate call instructions entirely.

The real power of this pass is that it provides context-sensitive mod/ref
information for call instructions. This allows the optimizer to know that calls
to a function do not clobber or read the value of the global, allowing loads and
stores to be eliminated.

Note

This pass is somewhat limited in its scope (only support non-address taken
globals), but is very quick analysis.

The -steens-aa pass implements a variation on the well-known “Steensgaard’s
algorithm” for interprocedural alias analysis. Steensgaard’s algorithm is a
unification-based, flow-insensitive, context-insensitive, and field-insensitive
alias analysis that is also very scalable (effectively linear time).

The LLVM -steens-aa pass implements a “speculatively field-sensitive”
version of Steensgaard’s algorithm using the Data Structure Analysis framework.
This gives it substantially more precision than the standard algorithm while
maintaining excellent analysis scalability.

Note

-steens-aa is available in the optional “poolalloc” module. It is not part
of the LLVM core.

The -ds-aa pass implements the full Data Structure Analysis algorithm. Data
Structure Analysis is a modular unification-based, flow-insensitive,
context-sensitive, and speculatively field-sensitive alias
analysis that is also quite scalable, usually at O(n*log(n)).

This algorithm is capable of responding to a full variety of alias analysis
queries, and can provide context-sensitive mod/ref information as well. The
only major facility not implemented so far is support for must-alias
information.

Note

-ds-aa is available in the optional “poolalloc” module. It is not part of
the LLVM core.

The -scev-aa pass implements AliasAnalysis queries by translating them into
ScalarEvolution queries. This gives it a more complete understanding of
getelementptr instructions and loop induction variables than other alias
analyses have.

The -licm pass implements various Loop Invariant Code Motion related
transformations. It uses the AliasAnalysis interface for several different
transformations:

It uses mod/ref information to hoist or sink load instructions out of loops if
there are no instructions in the loop that modifies the memory loaded.

It uses mod/ref information to hoist function calls out of loops that do not
write to memory and are loop-invariant.

If uses alias information to promote memory objects that are loaded and stored
to in loops to live in a register instead. It can do this if there are no may
aliases to the loaded/stored memory location.

The -argpromotion pass promotes by-reference arguments to be passed in
by-value instead. In particular, if pointer arguments are only loaded from it
passes in the value loaded instead of the address to the function. This pass
uses alias information to make sure that the value loaded from the argument
pointer is not modified between the entry of the function and any load of the
pointer.

The -print-alias-sets pass is exposed as part of the opt tool to print
out the Alias Sets formed by the AliasSetTracker class. This is useful if
you’re using the AliasSetTracker class. To use it, use something like:

The -count-aa pass is useful to see how many queries a particular pass is
making and what responses are returned by the alias analysis. As an example:

% opt -basicaa -count-aa -ds-aa -count-aa -licm

will print out how many queries (and what responses are returned) by the
-licm pass (of the -ds-aa pass) and how many queries are made of the
-basicaa pass by the -ds-aa pass. This can be useful when debugging a
transformation or an alias analysis implementation.

The -aa-eval pass simply iterates through all pairs of pointers in a
function and asks an alias analysis whether or not the pointers alias. This
gives an indication of the precision of the alias analysis. Statistics are
printed indicating the percent of no/may/must aliases found (a more precise
algorithm will have a lower number of may aliases).

If you’re just looking to be a client of alias analysis information, consider
using the Memory Dependence Analysis interface instead. MemDep is a lazy,
caching layer on top of alias analysis that is able to answer the question of
what preceding memory operations a given instruction depends on, either at an
intra- or inter-block level. Because of its laziness and caching policy, using
MemDep can be a significant performance win over accessing alias analysis
directly.